A global reanalysis of vegetation phenology

Simulations of the global water and carbon cycle are sensitive to the model representation of vegetation phenology. Current phenology models are empirical, and few predict both phenological timing and leaf state. Our previous study demonstrated how satellite data assimilation employing an Ensemble Kalman Filter yields realistic phenological model parameters for several ecosystem types. In this study the data assimilation framework is extended to global scales using a subgrid‐scale representation of plant functional types (PFTs) and elevation classes. A reanalysis of vegetation phenology for 256 globally distributed regions is performed using 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of photosynthetically active radiation (FPAR) absorbed by vegetation and leaf area index (LAI) data. The 9 · 10 8 quality screened observations (corresponding to <1% of the globally available MODIS data) successfully constrain a posterior PFT‐dependent phenological parameter set. It reduces the global FPAR and LAI prediction error to 20.6% and 14.8%, respectively, compared to the prior prediction error. A 50 year long (1960–2009) daily 1° × 1° global phenology data set with a mean FPAR and LAI prediction error of 0.065 (−) and 0.34 (m 2 m −2 ) is generated. Temperate phenology is best explained by a combination of light and temperature. Tropical evergreen phenology is found to be largely insensitive to moisture and light variations. Boreal phenology can be accurately predicted from local to global scales, while temperate and mediterranean landscapes might benefit from a better subgrid‐scale PFT classification or from a more complex canopy radiative transfer model.